Dynamic response of a vehicle with planar suspension system (PSS) under differential braking
Bibliographic record
Abstract
To absorb the vibrations and shocks caused by road obstacles effectively in any direction within the wheel rotation plane, a planar suspension system (PSS), in which there are spring-damper struts in both the vertical and longitudinal directions, is proposed to improve the ride quality of a vehicle with such novel suspension systems. The longitudinal spring-damper strut in a PSS is considerably soft compared with the longitudinal connection in a conventional suspension. Consequently, the wheels in a vehicle with PSS can move forth and back with respect to the body. The dynamic behaviours of a PSS vehicle under some special conditions, such as a differential braking in which the braking torque applied to the wheels at two sides of an axle are uneven, may exhibit special characteristics. The directional stability of the PSS vehicle in such a case may be one of the major concerns. The dynamic performance of the PSS vehicle in the differential braking condition is thus necessary to be investigated. This paper presents the investigation results of the transient response of a vehicle with the PSS in such a case. The simulation results are also compared with those of a similar vehicle with conventional suspensions. The study demonstrates that the PSS vehicle is directionally stable in differential braking conditions. The dynamic behaviour of the PSS vehicle is generally comparable with that of a conventional vehicle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".